Insurance underwriting is the process of assessing the value of a given risk, and in turn pricing a policy to protect against that risk. Fundamentally, insurance premiums are designed to reflect the amount of a payout should a covered event occur in view of the likelihood of the occurrence of that event. The process of determining the cost of an insurance policy is called rating. The rating process may include a number of variables, including experience data for a specific insured, experience data for a class of insured entities, capital investment predictions, profit margin targets, and a wide variety of other data useful for predicting the occurrence of certain real-world events as well as the amount of damage likely to result from such events.
Further, “experience rating” involves analyzing past claims experience to determine a prospective premium amount and/or a retrospective premium adjustment. See, e.g., P. Booth, “Modern Actuarial Theory and Practice,” 340-51 (Chapman & Hall/CRC 1999). For example, a business may operate a large fleet of vehicles. And, that business may seek to insure the vehicles to cover property damage and to cover possible personal injury claims if a fleet vehicle were to be in an accident with another vehicle. If the fleet is large enough or the business has been operating the fleet long enough, there may be enough historical data to reliably and accurately estimate the expected claims for the next year. That estimate (possibly combined with an allocation of expenses or assessment of an administrative fee) would represent the insurance premium in an ideal scenario. At the end of the annual policy term, a surcharge or refund may also be appropriate if the actual claims for the term were higher or lower than the estimated claims amount.
A typical family seeking automobile insurance cannot, however, produce anywhere near the amount of data needed to make a reliable and accurate estimate of anticipated claims for their vehicle or vehicles. Thus, insurance companies must rate personal policies in a risk pool of comparable policies to produce enough data to make such an estimate. One mechanism for doing this is to assess what data is available for the family (e.g., demographic information, types of vehicles, and what limited claim information is available) and use that data to assign an appropriate pool to the family.
The myriad types of data available to an insurer for performing the rating process are often associated with geographic locations or regions. However, this association is not consistent or uniform. Some property crime data is associated with a “block” of addresses on a city street, e.g., 300-400 block of Main Street. Flood zone data and land elevation data may be stored as complex topographic maps. Loss experience data may be associated with a coordinate pair representing the longitude and latitude of the location of the loss event.
At present, insurance rating requires a complex search process to compile relevant data for input into a rating function. For example, a policy to be rated may be associated with a specific location, e.g., a street address of a home or office or the location where a vehicle will be parked at night. To rate a policy for that location, some subset of the relevant data must be gathered and provided to a rating algorithm. The gathering process is often computationally difficult in view of the inconsistent and non-uniform associations of data to geography discussed above. In some instances, data is processed and aggregated by county, city, and/or postal ZIP code. This aggregation is made difficult by the nearly arbitrary boundaries defined by county lines, city limits, and ZIP codes. Further, county, city, and postal ZIP code boundaries may change over time. In other instances, data is processed by aggregated sales territory.
Rates appropriated to each area are generally determined based on the associated historical claims experience. While existing methods of territorial rating have served insurance providers well, these approaches can be problematic for several reasons: (1) geographic boundaries can change, as discussed above; (2) geographic areas may be larger than desired; (3) populations may not be equally distributed within these geographic areas; (4) historical claim experience within these geographic areas may be limited; and (5) where a vehicle is garaged does not accurately measure geographic risk of where the vehicle is used.